Predictive microservices activation using machine learning

ABSTRACT

Described are techniques for predictive microservice activation. The techniques include training a machine learning model using a plurality of sequences of coordinates, where the plurality of sequences of coordinates are respectively based upon a corresponding plurality of series of vectors generated from historical usage data for an application and its associated microservices. The techniques further include inputting a new sequence of coordinates representing a series of application operations to the machine learning model. The techniques further include identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model. The techniques further include activating the predicted microservice prior to the predicted microservice being called by the application.

BACKGROUND

The present disclosure relates to microservices, and, more specifically,to predictive microservices activation using machine learning.

Microservices are a cloud native architectural approach whereby anapplication is composed of multiple loosely coupled and separatelydeployable smaller components, where the smaller components can bereferred to as services or microservices. Microservices can each beassociated with a stack, communicate with each other via eventstreaming, message brokers, and/or Representational State Transfer(REST) Application Programming Interfaces (APIs), and/or be organized bybusiness capability.

Microservices provide numerous benefits and advantages. For one, usingmicroservices makes source code easier to update. For another,developers can use different stacks having characteristics suited todifferent microservices. Finally, microservices can be scaledindependently of each other, thereby improving computational efficiencyby enabling a frequently used microservice of an application to bescaled rather than requiring the application be scaled in its entirety.

SUMMARY

Aspects of the present disclosure are directed toward acomputer-implemented method comprising training a machine learning modelusing a plurality of sequences of coordinates, where the plurality ofsequences of coordinates are respectively based upon a correspondingplurality of series of vectors generated from historical usage data foran application and its associated microservices. The method furthercomprises inputting a new sequence of coordinates representing a seriesof application operations to the machine learning model. The methodfurther comprises identifying a predicted microservice for futureutilization based on an output vector generated by the machine learningmodel. The method further comprises activating the predictedmicroservice prior to the predicted microservice being called by theapplication.

Additional aspects of the present disclosure are directed to systems andcomputer program products configured to perform the methods describedabove. The present summary is not intended to illustrate each aspect of,every implementation of, and/or every embodiment of the presentdisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated intoand form part of the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 illustrates a block diagram of an example computationalenvironment for predictive microservices activation, in accordance withsome embodiments of the present disclosure.

FIG. 2 illustrates a diagram of an example sequence of operationsrepresented as vectors, in accordance with some embodiments of thepresent disclosure.

FIG. 3A illustrates an example table of coordinates representingoperations, in accordance with some embodiments of the presentdisclosure.

FIG. 3B illustrates example sequences of coordinates based on respectiveseries of vectors corresponding to application operations, in accordancewith some embodiments of the present disclosure.

FIG. 4 illustrates a flowchart of an example method for predictivemicroservices activation, in accordance with some embodiments of thepresent disclosure.

FIG. 5 illustrates a flowchart of an example method for training a modelfor predictive microservices activation, in accordance with someembodiments of the present disclosure.

FIG. 6 illustrates a block diagram of an example computer, in accordancewith some embodiments of the present disclosure.

FIG. 7 depicts a cloud computing environment, in accordance with someembodiments of the present disclosure.

FIG. 8 depicts abstraction model layers, in accordance with someembodiments of the present disclosure.

While the present disclosure is amenable to various modifications andalternative forms, specifics thereof have been shown by way of example,in the drawings and will be described in detail. It should beunderstood, however, that the intention is not to limit the presentdisclosure to the particular embodiments described. On the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the present disclosure.

DETAILED DESCRIPTION

Aspects of the present disclosure are directed toward cloudmicroservices, and, more specifically, to predictive microservicesactivation using machine learning. While not limited to suchapplications, embodiments of the present disclosure may be betterunderstood in light of the aforementioned context.

Aspects of the present disclosure are directed toward predictivelyactivating microservices before they are called by an application.Predictively activating microservices improves application performanceby reducing latency associated with microservice initiation. Furtheraspects of the present disclosure deactivate or idle activemicroservices that are not predicted to be needed. Deactivating oridling active microservices that are not needed can improve performanceby freeing resources for the application. In predicting whichmicroservices to activate and deactivate, aspects of the presentdisclosure can utilize machine learning models such as, for example,recurrent neural networks (RNNs). Regardless of the type of machinelearning model used, training data can be based on line graphscorresponding to sequences of application operations, vectors of theline graphs, and/or sequences of coordinates based on the vectors). Inorder to increase accuracy of the model over time, aspects of thepresent disclosure can provide feedback to the machine learning model touse as additional training data based on accurate and/or inaccuratepredictions made by the machine learning model. Finally, in situationswhere the machine learning model may make an incorrect prediction andfail to proactively activate a needed microservice, aspects of thepresent disclosure can include a dynamic buffer pool configured to holdreserved resources capable of expeditiously activating any neededmicroservice that was not predicted.

Collectively, aspects of the present disclosure realize (i) reducedlatency (by proactively activating microservices before they arecalled); and/or (ii) improved resource efficiency (by proactivelydeactivating microservices that are not anticipated to be used) relativeto technologies not employing predictive microservice activationmethodologies. Furthermore, even relative to any known predictivemicroservice activation methodologies, aspects of the present disclosureexhibit improved performance through (i) improved accuracy (e.g., due tofeedback-based re-training of the machine learning model); (ii) improvedmodel efficiency (e.g., by formatting training data using line graphs,vectors of line graphs, and/or sequences of coordinates based on thevectors); and/or (iii) improved redundancy (e.g., a dynamic buffer poolfor promptly activating unpredicted microservices).

Referring now to the figures, FIG. 1 illustrates a block diagram of acomputational environment 100 for predictive microservices activation.The computational environment 100 includes a predictive microservicesactivation system 102 having stored therein (or communicatively coupledthereto) a model 104, an event processor 112, and a dynamic buffer pool120.

The model 104 can include input vector formatter 106 for convertingtraining data 110 and/or new data 128 into a format ingestible by amachine learning model 108. The machine learning model 108 can be basedon one or more machine-learning algorithms. Machine-learning algorithmscan include, but are not limited to, decision tree learning, associationrule learning, artificial neural networks (ANN), RNNs, deep learning,inductive logic programming, support vector machines, clustering,Bayesian networks, reinforcement learning, representation learning,similarity/metric training, sparse dictionary learning, geneticalgorithms, rule-based learning, and/or other machine learningtechniques.

For example, the machine learning algorithms can utilize one or more ofthe following example techniques: K-nearest neighbor (KNN), learningvector quantization (LVQ), self-organizing map (SOM), logisticregression, ordinary least squares regression (OLSR), linear regression,stepwise regression, multivariate adaptive regression spline (MARS),ridge regression, least absolute shrinkage and selection operator(LASSO), elastic net, least-angle regression (LARS), probabilisticclassifier, naïve Bayes classifier, binary classifier, linearclassifier, hierarchical classifier, canonical correlation analysis(CCA), factor analysis, independent component analysis (ICA), lineardiscriminant analysis (LDA), multidimensional scaling (MDS),non-negative metric factorization (NMF), partial least squaresregression (PLSR), principal component analysis (PCA), principalcomponent regression (PCR), Sammon mapping, t-distributed stochasticneighbor embedding (t-SNE), bootstrap aggregating, ensemble averaging,gradient boosted decision tree (GBRT), gradient boosting machine (GBM),inductive bias algorithms, Q-learning, state-action-reward-state-action(SARSA), temporal difference (TD) learning, apriori algorithms,equivalence class transformation (ECLAT) algorithms, Gaussian processregression, gene expression programming, group method of data handling(GMDH), inductive logic programming, instance-based learning, logisticmodel trees, information fuzzy networks (IFN), hidden Markov models,Gaussian naïve Bayes, multinomial naïve Bayes, averaged one-dependenceestimators (AODE), Bayesian network (BN), classification and regressiontree (CART), chi-squared automatic interaction detection (CHAID),expectation-maximization algorithm, feedforward neural networks, logiclearning machine, self-organizing map, single-linkage clustering, fuzzyclustering, hierarchical clustering, Boltzmann machines, convolutionalneural networks, recurrent neural networks, hierarchical temporal memory(HTM), and/or other machine learning techniques.

The machine learning model 108 can be trained on the training data 110for predicting future microservice usage given a series of operations(e.g., new data 128 from real-time operation of an application 126).Advantageously, predicting future microservice usage can enable thepredictive microservice activation system 102, the application 126,and/or another aspect of the present disclosure to activate, initiate,or awaken one or more predicted microservices 116 before they are calledby the application 126. Proactively activating predicted microservices116 can cause the application 126 to function more effectively byreducing time associated with activating a microservice. Accordingly, anapplication 126 utilizing aspects of the present disclosure canexperience reduced latency and improved performance.

The training data 110 can comprise historical usage data for theapplication 126. In some embodiments, the input vector formatter 106formats the training data 110 by generating line graphs of historicalsequences of operations implemented by the application 126. In someembodiments, the line graphs are converted to a series of vectors, whereeach vector is a directed line from a first operation to a subsequentoperation. In some embodiments, the series of vectors are converted to asequence of coordinates, where each of the coordinates corresponds to adifference of a coordinate corresponding to the first operationsubtracted from a coordinate corresponding to the subsequent operation.Training data 110 formatted according to the input vector formatter 106is discussed in more detail hereinafter with respect to FIGS. 2, 3A, 3B,and 5.

Machine learning model 108 can be trained on the training data 110.After training, the machine learning model 108 can ingest new data 128(e.g., generated from real-time usage) of the application 126. Themachine learning model 108 can generate an output vector 114 based onthe new data 128. The output vector 114 can be provided to the eventprocessor 112 for the purposes of converting the output vector intointelligible information, such as information indicative of predictedmicroservices 116 and/or predicted pods 118. As is understood by oneskilled in the art, pods can be utilized in Kubernetes® implementationsfor treating multiple containers as a single unit of deployment.Accordingly, in some embodiments, a single pod can be associated withmultiple microservices, or vice versa, a single microservice can beassociated with multiple pods. As a result, in embodiments utilizingpredicted pods 118, the predicted pods 118 can be used as a mechanismfor activating the predicted microservices 116.

The event processor 112 can provide an indication of the predictedmicroservices 116 and/or the predicted pods 118 to the application 126so that the application 126 can proactively activate the predictedmicroservices 116.

If the predicted microservices 116 are determined to be accurate (e.g.,the predicted microservices 116 are called by the application 126 withina threshold time period of proactively activating the predictedmicroservices 116), then this feedback can be provided back to the model104 as supplemental, additional, or new training data 110 (e.g., wherethe new data 128 can comprise the input vector, and the predictedmicroservices 116 can comprise the outcome or a subsequent operation).In contrast, if the predicted microservices 116 are determined to beinaccurate (e.g., the predicted microservices 116 are not called by theapplication 126 within the threshold time period of proactivelyactivating the predicted microservices 116), then this feedback can beprovided back to the model 104 as supplemental, additional, or newtraining data 110 (e.g., where the new data 128 can comprise the inputvector, and the actually called microservices—rather than the predictedmicroservices 116—can comprise the outcome or a subsequent operation).In some embodiments, inaccurate predictions can trigger re-training ofthe machine learning model 108 using the feedback. In other embodiments,the machine learning model 108 can be re-trained using any collectedfeedback at static or dynamic intervals.

In situations where the predicted microservices 116 are not accurate,the predictive microservices activation system 102 can utilize thedynamic buffer pool 120 to expediently activate one or more emergencymicroservices 122, where the emergency microservices 122 can be themicroservices actually called by the application 126. The dynamic bufferpool 120 can hold memory, processing, bandwidth, and/or other resourcesin reserve as a backup for efficiently activating emergencymicroservices 122 in situations where the predicted microservices 116are incorrect. Over time, as the model machine learning model 108 isre-trained using feedback from correct and incorrect predictedmicroservices 116, the machine learning model 108 becomes increasinglyaccurate, and the amount of resources reserved by the dynamic bufferpool 120 can be decreased.

Although predictive microservices activation system 102 is showncommunicatively coupled to a computer system 124 executing theapplication 126 by a network 130, in other embodiments, the predictivemicroservices activation system 102 is incorporated into (e.g.,downloaded to, installed on, and executed on) the same computer system124 implementing the application 126. In yet other embodiments, thepredictive microservices activation system 102 can be a service providedto numerous applications running on numerous physical or virtualcomputer systems. In yet other embodiments, the predictive microservicesactivation system 102 can split its functionality between a remoteserver (not shown) and the physical or virtual computer system 124implementing the application 126. In such embodiments, the remote servercan be configured to retrieve training data 110 from the application126, train the machine learning model 108, and provide the trainedmachine learning model 108 to the application 126 for storage andexecution on resources of the computer system 124. Periodically, theremote server can retrain the machine learning model 108 using new data128 collected from the application 126, and the remote server andprovide updated versions of the machine learning model 108 to theapplication 126 to replace the previous version stored on and executedby the computer system 124.

The predictive microservices activation system 102 and the computersystem 124 can be any system having a computer-readable storage mediumand a processor capable of executing instructions stored on thecomputer-readable storage medium. For example, the predictivemicroservices activation system 102 and the computer system 124 can be adesktop, server, tablet, smartphone, virtual machine, or another type ofcomputer system. The application 126 can be any computer-implementedapplication now known or later developed such as, but not limited to, anembedded system, a desktop application, a web application, a web serviceapplication, a console application, a cloud application (e.g.,infrastructure-as-a-service, platform-as-a-service,software-as-a-service, compute services, storage services, datamanagement services, networking services, etc.), and/or otherapplications.

Network 130 can be any network now known or later developed. Forexample, network 130 can be an intranet, the Internet, a wide areanetwork (WAN), a local area network (LAN), a personal area network(PAN), or a another type of network that physically or wirelesslyconnects (directly or indirectly) multiple data processing systemstogether on a permanent or intermittent basis.

FIG. 2 illustrates a diagram of an example sequence of operations 200represented as a series of vectors, in accordance with some embodimentsof the present disclosure. The sequence of operations 200 is composed offive points, each representing an operation of an application. Vectorsdirected from one point to another represent sequences of operations.Accordingly, point A (X₀, Y₀) 202 is a first operation, point B (X₁, Y₁)204 is a second operation subsequent to the first operation, point C(X₂, Y₂) 206 is a third operation subsequent to the second operation,point D (X₃, Y₃) 208 is a fourth operation subsequent to the thirdoperation, and point E (X₄, Y₄) 210 is a fifth operation subsequent tothe fourth operation.

As can be seen, the points are represented by two-dimensionalcoordinates arranged between two perpendicular axes—an x-axis 212 and ay-axis 214. In some embodiments, the coordinates of each pointrepresenting an operation are normalized so that the coordinates fallbetween −1, 1, inclusive, on the x-axis 212 and −1, 1, inclusive, on they-axis 214.

Normalization can be performed based on maximum and minimum values. Insome embodiments, the maximum and minimum values dynamically change as afunction of time insofar as the type of operations are executable in anapplication can change over time as the functionality of the applicationprogresses.

For example, normalized x-values can be determined using Equation 1:

$\begin{matrix}{x_{i} = \frac{X_{i} - X_{\min}}{X_{\max} - X_{i}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In Equation 1, x_(i) refers to the normalized x-value, X₁ refers to theactual x-value, X_(min) refers to the minimum x-value, and X_(max)refers to the maximum x-value. As previously discussed, X_(min) andX_(max) can be dynamically determined.

Similarly, normalized y-values can be determined using Equation 2:

$\begin{matrix}{y_{i} = \frac{Y_{i} - Y_{\min}}{Y_{\max} - Y_{i}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

In Equation 2, y_(i) refers to the normalized y-value, Y_(i) refers tothe actual y-value, Y_(min) refers to the minimum y-value, and Y_(max)refers to the maximum y-value. As previously discussed, Y_(min) andY_(max) can be dynamically determined.

FIG. 3A illustrates an example table 300 of coordinates representingoperations, in accordance with some embodiments of the presentdisclosure. The table 300 illustrates various iterations (e.g., 1, 2) ofdifferent sequences of operations (e.g., Step 1, Step 2, Step 3, Step4). As shown, the table 300 includes in a first iteration of operationsA (X₀, Y₀), B (X₁, Y₁), C (X₂, Y₂), and D (X₃, Y₃). The table 300further includes a second iteration of operations of B (X₁, Y₁), C (X₂,Y₂), D (X₃, Y₃), and a theoretical next operation M (X_(m-1), Y_(m-1)).In some embodiments, the iterations (e.g., 1, 2) can be input to amachine learning model as training data. As will be appreciated by oneskilled in the art, although the example table 300 includes variousiterations of four-step sequences, any number n-step sequences can beused beginning with n=2.

FIG. 3B illustrates an example table 310 of sequences of coordinatescorresponding to the iterations illustrated in table 300 of FIG. 3A. Insome embodiments, the sequences of coordinates are based on respectiveseries of vectors corresponding to application operations. In the firstsequence of coordinates, X₁, the first coordinate is (X₁-X₀, Y₁-Y₀). Inother words, the first coordinate is the coordinates of an initial(e.g., first) operation subtracted from the coordinates of a subsequent(e.g., second) operation representing the vector of Step 1→ Step 2(e.g., A (X₀, Y₀) to B (X₁, Y₁)). The second and third coordinates inthe first sequence of coordinates, X₁, correspond to the differences incoordinates of Step 2→ Step 3 and Step 3→ Step 4. The second sequence ofcoordinates, X₂, follows the same pattern but according to the seconditeration in the table 300 of FIG. 3A. For example, the vector of Step1→ Step 2 corresponds to coordinates B (X₁, Y₁) to C (X₂, Y₂).Accordingly, the first coordinate of the second sequence of coordinates,X₂, corresponds to the coordinates of B subtracted from C, or (X₂-X₁,Y₂-Y₁). In some embodiments, the sequences of coordinates illustrated inthe example table 310 of FIG. 3B can be used as training data for themachine learning model.

FIG. 4 illustrates a flowchart of an example method 400 for predictivemicroservices activation, in accordance with some embodiments of thepresent disclosure. The method 400 can be implemented by a computer, aprocessor, a data processing system, a server, a predictivemicroservices activation system 102, or another configuration ofhardware and/or software.

Operation 402 includes training a machine learning model using aplurality of sequences of coordinates (e.g., the sequences ofcoordinates illustrated in the example table 300 of FIG. 3A or table 310of FIG. 3B). Operation 402 is discussed in more detail hereinafter withrespect to FIG. 5.

Operation 404 includes inputting a new sequence of coordinatesrepresenting a real-time series of operations of an application to themachine learning model.

Operation 406 includes identifying one or more predicted microservicesbased on an output vector generated by the machine learning model. Insome embodiments, the one or more predicted microservices are identifiedvia one or more pods associated with the one or more predictedmicroservices.

Operation 408 includes activating the one or more predictedmicroservices. In embodiments including pods of microservices, operation408 can include activating the predicted microservice via one or morepods associated with the predicted microservice. In some embodiments,operation 408 includes utilizing, by the application, the one or morepredicted microservices within a threshold amount of time of activatingthe one or more predicted microservices.

Operation 410 includes deactivating (e.g., idling, hibernating, etc.)another microservice that is activated but is not associated with theone or more predicted microservices identified in operation 406.Advantageously, aspects of the present disclosure can predictivelyactivate soon-to-be-used microservices and proactively deactivatemicroservices which are unnecessarily active (insofar as the machinelearning model predicts that they will not be utilized within athreshold period of time). Deactivating unnecessarily active microservices can improve performance of an application by efficiently usingresources associated with the application.

Operation 412 includes determining if the one or more predictedmicroservices were, in fact, called by the application within athreshold period of time of activating the one or more predictedmicroservices. If the one or more predicted microservices are calledwithin the threshold period of time (412: YES), then the method 400proceeds to operation 414 and adds the sequence of coordinates and theone or more predicted microservices as training data to the machinelearning model. If the one or more predicted microservices are notcalled within the threshold period of time (412: NO), then the method400 can proceed to operation 416 and utilize a dynamic buffer pool toactivate, using the reserved resources of the dynamic buffer pool, thecorrect microservices. The method 400 can then proceed to operation 418which includes providing feedback to the machine learning model forcorrective training.

FIG. 5 illustrates a flowchart of an example method 500 for training amodel for predictive microservices activation, in accordance with someembodiments of the present disclosure. The method 500 can be implementedby a computer, a processor, a data processing system, a server, apredictive microservices activation system 102, or another configurationof hardware and/or software. In some embodiments, the method 500 is asub-method of operation 402 of FIG. 4.

Operation 502 includes converting historical usage of an application toline graphs representing sequences of operations. In some embodiments,operation 502 includes converting operations to coordinates using anymulti-dimensional coordinate scheme from two-dimensional Euclideancoordinates (e.g., (x,y)) to multi-dimensional coordinates usingEuclidean, Spherical, Polar, or a different coordinate format. FIG. 2illustrates an example line graph composed of numerous vectors.

Operation 504 includes converting each line graph to a respective seriesof vectors, where each vector can be associated with a consecutive pairof operations, and where each vector extends from an initial operationto a subsequent operation. Discrete vectors are illustrated as portionsof the line graph of FIG. 2 and represented as transitions between stepsin the table 300 of FIG. 3A.

Operation 506 includes generating sequences of coordinates representingeach series of vectors. In some embodiments, the coordinates can bederived by subtracting coordinates of an initial (e.g., first) operationfrom coordinates of a subsequent (e.g., second operation). In someembodiments, operation 506 generates sequences of coordinates consistentwith the example table 310 illustrated in operation 3B.

Operation 508 includes training the machine learning model on thegenerated sequences of coordinates. The machine learning model can betrained according to any number of methods and techniques, some of whichare previously discussed with respect to FIG. 1.

FIG. 6 illustrates a block diagram of an example computer 600 inaccordance with some embodiments of the present disclosure. In variousembodiments, computer 600 can perform any or all of the methodsdescribed in FIGS. 4-5 and/or implement the functionality discussed inone or more of FIGS. 1-3. In some embodiments, computer 600 receivesinstructions related to the aforementioned methods and functionalitiesby downloading processor-executable instructions from a remote dataprocessing system via network 650. In other embodiments, computer 600provides instructions for the aforementioned methods and/orfunctionalities to a client machine such that the client machineexecutes the method, or a portion of the method, based on theinstructions provided by computer 600. In some embodiments, the computer600 is incorporated into (or functionality similar to computer 600 isvirtually provisioned to) one or more entities of the computationalenvironment 100 (e.g., predictive microservices activation system 102,computer system 124) and/or other aspects of the present disclosure.

Computer 600 includes memory 625, storage 630, interconnect 620 (e.g.,BUS), one or more CPUs 605 (also referred to as processors herein), I/Odevice interface 610, I/O devices 612, and network interface 615.

Each CPU 605 retrieves and executes programming instructions stored inmemory 625 or storage 630. Interconnect 620 is used to move data, suchas programming instructions, between the CPUs 605, I/O device interface610, storage 630, network interface 615, and memory 625. Interconnect620 can be implemented using one or more busses. CPUs 605 can be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In some embodiments, CPU 605 can be adigital signal processor (DSP). In some embodiments, CPU 605 includesone or more 3D integrated circuits (3DICs) (e.g., 3D wafer-levelpackaging (3DWLP), 3D interposer based integration, 3D stacked ICs(3D-SICs), monolithic 3D ICs, 3D heterogeneous integration, 3D system inpackage (3DSiP), and/or package on package (PoP) CPU configurations).Memory 625 is generally included to be representative of a random-accessmemory (e.g., static random-access memory (SRAM), dynamic random-accessmemory (DRAM), or Flash). Storage 630 is generally included to berepresentative of a non-volatile memory, such as a hard disk drive,solid state device (SSD), removable memory cards, optical storage, orflash memory devices. In an alternative embodiment, storage 630 can bereplaced by storage area-network (SAN) devices, the cloud, or otherdevices connected to computer 600 via I/O device interface 610 ornetwork 650 via network interface 615.

In some embodiments, memory 625 stores instructions 660. However, invarious embodiments, instructions 660 are stored partially in memory 625and partially in storage 630, or they are stored entirely in memory 625or entirely in storage 630, or they are accessed over network 650 vianetwork interface 615.

Instructions 660 can be computer-readable and computer-executableinstructions for performing any portion of, or all of, the methods ofFIGS. 4-5 and/or implementing the functionality discussed in any portionof FIGS. 1-3. Although instructions 660 are shown in memory 625,instructions 660 can include program instructions collectively storedacross numerous computer-readable storage media and executable by one ormore CPUs 605.

In various embodiments, I/O devices 612 include an interface capable ofpresenting information and receiving input. For example, I/O devices 612can present information to a user interacting with computer 600 andreceive input from the user.

Computer 600 is connected to network 650 via network interface 615.Network 650 can comprise a physical, wireless, cellular, or differentnetwork.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 7, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 7 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 8, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 7) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 8 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents.

Examples of hardware components include: mainframes 61; RISC (ReducedInstruction Set Computer) architecture based servers 62; servers 63;blade servers 64; storage devices 65; and networks and networkingcomponents 66. In some embodiments, software components include networkapplication server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow.

Resource provisioning 81 provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing 82 provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and predictive microservices activation 96.

Embodiments of the present invention can be a system, a method, and/or acomputer program product at any possible technical detail level ofintegration. The computer program product can include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or subsetof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While it is understood that the process software (e.g., any of theinstructions stored in instructions 660 of FIG. 6 and/or any softwareconfigured to perform any portion of the methods described with respectto FIGS. 4-5 and/or implement any portion of the functionality discussedin FIGS. 1-3) can be deployed by manually loading it directly in theclient, server, and proxy computers via loading a storage medium such asa CD, DVD, etc., the process software can also be automatically orsemi-automatically deployed into a computer system by sending theprocess software to a central server or a group of central servers. Theprocess software is then downloaded into the client computers that willexecute the process software. Alternatively, the process software issent directly to the client system via e-mail. The process software isthen either detached to a directory or loaded into a directory byexecuting a set of program instructions that detaches the processsoftware into a directory. Another alternative is to send the processsoftware directly to a directory on the client computer hard drive. Whenthere are proxy servers, the process will select the proxy server code,determine on which computers to place the proxy servers' code, transmitthe proxy server code, and then install the proxy server code on theproxy computer. The process software will be transmitted to the proxyserver, and then it will be stored on the proxy server.

Embodiments of the present invention can also be delivered as part of aservice engagement with a client corporation, nonprofit organization,government entity, internal organizational structure, or the like. Theseembodiments can include configuring a computer system to perform, anddeploying software, hardware, and web services that implement, some orall of the methods described herein. These embodiments can also includeanalyzing the client's operations, creating recommendations responsiveto the analysis, building systems that implement subsets of therecommendations, integrating the systems into existing processes andinfrastructure, metering use of the systems, allocating expenses tousers of the systems, and billing, invoicing (e.g., generating aninvoice), or otherwise receiving payment for use of the systems.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the variousembodiments. As used herein, the singular forms “a,” “an,” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“includes” and/or “including,” when used in this specification, specifythe presence of the stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. In the previous detaileddescription of example embodiments of the various embodiments, referencewas made to the accompanying drawings (where like numbers represent likeelements), which form a part hereof, and in which is shown by way ofillustration specific example embodiments in which the variousembodiments can be practiced. These embodiments were described insufficient detail to enable those skilled in the art to practice theembodiments, but other embodiments can be used and logical, mechanical,electrical, and other changes can be made without departing from thescope of the various embodiments. In the previous description, numerousspecific details were set forth to provide a thorough understanding thevarious embodiments. But the various embodiments can be practicedwithout these specific details. In other instances, well-known circuits,structures, and techniques have not been shown in detail in order not toobscure embodiments.

Different instances of the word “embodiment” as used within thisspecification do not necessarily refer to the same embodiment, but theycan. Any data and data structures illustrated or described herein areexamples only, and in other embodiments, different amounts of data,types of data, fields, numbers and types of fields, field names, numbersand types of rows, records, entries, or organizations of data can beused. In addition, any data can be combined with logic, so that aseparate data structure may not be necessary. The previous detaileddescription is, therefore, not to be taken in a limiting sense.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Although the present disclosure has been described in terms of specificembodiments, it is anticipated that alterations and modification thereofwill become apparent to the skilled in the art. Therefore, it isintended that the following claims be interpreted as covering all suchalterations and modifications as fall within the true spirit and scopeof the disclosure.

Any advantages discussed in the present disclosure are exampleadvantages, and embodiments of the present disclosure can exist thatrealize all, some, or none of any of the discussed advantages whileremaining within the spirit and scope of the present disclosure.

A non-limiting list of examples are provided hereinafter to demonstratesome aspects of the present disclosure. Example 1 is acomputer-implemented method. The method includes training a machinelearning model using a plurality of sequences of coordinates, whereinthe plurality of sequences of coordinates are respectively based upon acorresponding plurality of series of vectors generated from historicalusage data for an application and its associated microservices;inputting a new sequence of coordinates representing a series ofapplication operations to the machine learning model; identifying apredicted microservice for future utilization based on an output vectorgenerated by the machine learning model; and activating the predictedmicroservice prior to the predicted microservice being called by theapplication.

Example 2 includes the method of example 1, including or excludingoptional features. In this example, the machine learning model is arecurrent neural network (RNN).

Example 3 includes the method of any one of examples 1 to 2, includingor excluding optional features. In this example, the method includesdeactivating a second microservice that is not a predicted microserviceaccording to the output vector.

Example 4 includes the method of any one of examples 1 to 3, includingor excluding optional features. In this example, training the machinelearning model further comprises: converting the historical usage datato line graphs representing sequences of application operations;converting each line graph to a respective series of vectors; convertingeach series of vectors to a sequence of coordinates; and training themachine learning model based on respective sequences of coordinates.Optionally, the respective series of vectors includes, for each vector,a line directed from a first point corresponding to a first operation toa second point corresponding to a subsequent operation, and wherein acorresponding sequence of coordinates includes coordinates of the firstoperation subtracted from coordinates of the subsequent operation foreach vector in the respective series of vectors. Optionally, thehistorical usage data is normalized such that each of the coordinates isbetween −1 and 1, inclusive, on an x-axis and −1 and 1, inclusive, on ay-axis.

Example 5 includes the method of any one of examples 1 to 4, includingor excluding optional features. In this example, the method includesutilizing the microservice by the application; in response to utilizingthe microservice by the application, providing the series of applicationoperations and the predicted microservice as feedback to the machinelearning model.

Example 6 includes the method of any one of examples 1 to 5, includingor excluding optional features. In this example, the method includesdetermining that a different microservice that is not the predictedmicroservice is called by the application; activating the differentmicroservice using a dynamic buffer pool of emergency microservices; andproviding the series of application operations, the predictedmicroservice, and the different microservice as feedback to the machinelearning model. Optionally, the method includes re-training the machinelearning model using the feedback.

Example 7 includes the method of any one of examples 1 to 6, includingor excluding optional features. In this example, the series ofapplication operations are real-time operations.

Example 8 includes the method of any one of examples 1 to 7, includingor excluding optional features. In this example, activating thepredicted microservice further comprises activating a plurality of podsassociated with the predicted microservice.

Example 9 includes the method of any one of examples 1 to 8, includingor excluding optional features. In this example, the method is performedby one or more computers according to software that is downloaded to theone or more computers from a remote data processing system. Optionally,the method further comprises: metering a usage of the software; andgenerating an invoice based on metering the usage.

Example 10 is a system. The system includes one or more processors; andone or more computer-readable storage media storing program instructionswhich, when executed by the one or more processors, are configured tocause the one or more processors to perform a method according to anyone of examples 1 to 9.

Example 11 is a computer program product. The computer program productcomprising one or more computer readable storage media, and programinstructions collectively stored on the one or more computer readablestorage media, the program instructions comprising instructionsconfigured to cause one or more processors to perform a method accordingto any one of examples 1 to 9.

What is claimed is:
 1. A computer-implemented method comprising:training a machine learning model using a plurality of sequences ofcoordinates, wherein the plurality of sequences of coordinates arerespectively based upon a corresponding plurality of series of vectorsgenerated from historical usage data for an application and itsassociated microservices; inputting a new sequence of coordinatesrepresenting a series of application operations to the machine learningmodel; identifying a predicted microservice for future utilization basedon an output vector generated by the machine learning model; andactivating the predicted microservice prior to the predictedmicroservice being called by the application.
 2. The method of claim 1,wherein the machine learning model is a recurrent neural network (RNN).3. The method of claim 1, further comprising: deactivating a secondmicroservice that is not a predicted microservice according to theoutput vector.
 4. The method of claim 1, wherein training the machinelearning model further comprises: converting the historical usage datato line graphs representing sequences of application operations;converting each line graph to a respective series of vectors; convertingeach series of vectors to a sequence of coordinates; and training themachine learning model based on respective sequences of coordinates. 5.The method of claim 4, wherein the respective series of vectorsincludes, for each vector, a line directed from a first pointcorresponding to a first operation to a second point corresponding to asubsequent operation, and wherein a corresponding sequence ofcoordinates includes coordinates of the first operation subtracted fromcoordinates of the subsequent operation for each vector in therespective series of vectors.
 6. The method of claim 4, wherein thehistorical usage data is normalized such that each of the coordinates isbetween −1 and 1, inclusive, on an x-axis and −1 and 1, inclusive, on ay-axis.
 7. The method of claim 1, further comprising: utilizing themicroservice by the application; in response to utilizing themicroservice by the application, providing the series of applicationoperations and the predicted microservice as feedback to the machinelearning model.
 8. The method of claim 1, further comprising:determining that a different microservice that is not the predictedmicroservice is called by the application; activating the differentmicroservice using a dynamic buffer pool of emergency microservices; andproviding the series of application operations, the predictedmicroservice, and the different microservice as feedback to the machinelearning model.
 9. The method of claim 8, further comprising:re-training the machine learning model using the feedback.
 10. Themethod of claim 1, wherein the series of application operations arereal-time operations.
 11. The method of claim 1, wherein activating thepredicted microservice further comprises activating a plurality of podsassociated with the predicted microservice.
 12. The method of claim 1,wherein the method is performed by one or more computers according tosoftware that is downloaded to the one or more computers from a remotedata processing system.
 13. The method of claim 12, wherein the methodfurther comprises: metering a usage of the software; and generating aninvoice based on metering the usage.
 14. A system comprising: one ormore processors; and one or more computer-readable storage media storingprogram instructions which, when executed by the one or more processors,are configured to cause the one or more processors to perform a methodcomprising: training a machine learning model using a plurality ofsequences of coordinates, wherein the plurality of sequences ofcoordinates are respectively based upon a corresponding plurality ofseries of vectors generated from historical usage data for anapplication and its associated microservices; inputting a new sequenceof coordinates representing a series of application operations to themachine learning model; identifying a predicted microservice for futureutilization based on an output vector generated by the machine learningmodel; and activating the predicted microservice prior to the predictedmicroservice being called by the application.
 15. The system of claim14, wherein the machine learning model is a recurrent neural network(RNN).
 16. The system of claim 14, wherein the method further comprises:deactivating a second microservice that is not a predicted microserviceaccording to the output vector.
 17. The system of claim 14, whereintraining the machine learning model further comprises: converting thehistorical usage data to line graphs representing sequences ofapplication operations; converting each line graph to a respectiveseries of vectors; converting each series of vectors to a sequence ofcoordinates; and training the machine learning model based on respectivesequences of coordinates; wherein the historical usage data isnormalized such that each of the coordinates is between −1 and 1,inclusive, on an x-axis and −1 and 1, inclusive, on a y-axis, andwherein the respective series of vectors includes, for each vector, aline directed from a first point corresponding to a first operation to asecond point corresponding to a subsequent operation, and wherein acorresponding sequence of coordinates includes coordinates of the firstoperation subtracted from coordinates of the subsequent operation foreach vector in the respective series of vectors.
 18. The system of claim14, wherein the method further comprises: determining that a differentmicroservice that is not the predicted microservice is called by theapplication; activating the different microservice using a dynamicbuffer pool of emergency microservices; providing the series ofapplication operations, the predicted microservice, and the differentmicroservice as feedback to the machine learning model; and re-trainingthe machine learning model using the feedback.
 19. The system of claim14, wherein activating the predicted microservice further comprisesactivating a plurality of pods associated with the predictedmicroservice.
 20. A computer program product comprising one or morecomputer readable storage media, and program instructions collectivelystored on the one or more computer readable storage media, the programinstructions comprising instructions configured to cause one or moreprocessors to perform a method comprising: training a machine learningmodel using a plurality of sequences of coordinates, wherein theplurality of sequences of coordinates are respectively based upon acorresponding plurality of series of vectors generated from historicalusage data for an application and its associated microservices;inputting a new sequence of coordinates representing a series ofapplication operations to the machine learning model; identifying apredicted microservice for future utilization based on an output vectorgenerated by the machine learning model; and activating the predictedmicroservice prior to the predicted microservice being called by theapplication.